is pleased to present a three-day short course:

This three-day course is intended as both a theoretical and practical introduction to finite mixture modeling as it pertains to statistical methods regularly used in educational, behavioral, and social science research. An understanding of finite mixture modeling will be developed by relating it to participants' existing knowledge of traditional statistical methods (i.e., group comparison procedures, multiple regression for continuous and categorical outcomes, MANOVA); thus it is assumed that participants have exposure to multivariate statistical methods and analyses falling under the General Linear Model (GLM) umbrella. A participant's experience in this workshop will be further enhanced by additional prior coursework or experience with advanced modeling techniques such as longitudinal modeling, factor analysis, item response theory, structural equation modeling (SEM), and multilevel modeling.

To introduce mixture modeling principles in familiar contexts, we will begin with group comparisons and move to regression, and then discuss finite mixtures of more complex models including factor analysis, structural equation models, longitudinal models, and multilevel models. Along the way, we will cover aspects of modeling including model construction and specification, graphical representations of models, maximum likelihood estimation via the expectation-maximization algorithm, evaluating hypotheses and data-model fit, model comparisons, and modeling in the presence of missing data. Although finite mixture modeling has proven advantageous across many disciplines, the examples used in presentations draw primarily from social science research, including the fields of education and psychology. Examples will be accompanied by annotated input and output using various software packages for estimating these models such as R and Mplus. Additional guided exercises will be available for participants to practice running such models on their own. While not a requirement, participants who have previous experience with R and/or Mplus, and have either of these software programs installed on their own laptop computer, are encouraged to bring their laptops to run the example code as it is being presented during the workshop.

Proceed up Campus Dr to the "M" circle, go halfway around the circle and continue on Campus Dr (second right after entering the circle) until you see Stamp Student Union on your right side.

VISITOR PARKING

Participants may park at the Union Lane Garage (located between the Adele H. Stamp Student Union and Cole Field House) for a daily fee. There are numerous metered spaces on campus but the University police are diligent about ticketing cars at expired meters as well as cars without appropriate stickers in reserved parking lots.

The Campus is conveniently located approximately 1 mile from the College Park-University of Maryland Metro Station. The stop is on the green line of the D.C. Metro System. The University of Maryland Shuttle Bus runs from the College Park Metro stop on a twenty-minute schedule through the Campus. Or, a brisk twenty minute walk up a moderate hill through the Campus will bring you to all locations.

Participants are responsible for arranging their own accommodations. For out-of-town guests, there are several sources of accommodations in the immediate area. Information about hotel pricing and reservations can be found at the web site: http://www.cvs.umd.edu/visitors/offcampus.html. Note that participants will need to make their own arrangements for transportation to and from campus.

Jeff Harring is an Associate Professor of Measurement, Statistics and Evaluation in the Department of Human Development and Quantitative Methodology at the University of Maryland, where he teaches coursework in statistical methods, simulation techniques, longitudinal models, and finite mixture modeling. His research interests include mixture modeling in the context of SEM and longitudinal models and has appeared in such journals as Structural Equation Modeling: A Multidisciplinary Journal, Psychological Methods, Multivariate Behavioral Research, Journal of Educational Measurement, and Journal of Educational and Behavioral Statistics. Additionally, Dr. Harring co-authored a book entitled, Comparing Groups Randomization and Bootstrap Methods Using R, which was published in 2011, and published a co-edited a volume entitled, Advances in Longitudinal Methods in the Social and Behavioral Sciences, in 2012. He is a past program chair of Division D, Section 2: Statistical Theory and Methods of the American Educational Research Association. Dr. Harring holds M.S. and Ph.D. degrees from the University of Minnesota. He may be reached at harring@umd.edu